Collaborative Research: Ensemble-Based Predictability, Sensitivity and Data Assimilation in PREDICT

协作研究：PREDICT 中基于集成的可预测性、敏感性和数据同化

• 批准号: 0848753
• 负责人: Sharanya Majumdar
• 金额: $ 21.45万
• 依托单位: University of Miami
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-09-01 至 2013-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0848753&HistoricalAwards=false
• 关键词: Collaborative; Research; Ensemble; Based; Predictability

This award is funded under the American Recovery and Reinvestment Act of 2009 (Public Law 111-5).This study is part of the Pre-Depression Investigation of Cloud-systems in the Tropics (PREDICT) project. The central scientific hypothesis of this study is that properly simulating the initial formation of synoptic-scale, pre-tropical-depression wave trough in numerical forecasting models is most essential for the accurate modeling of tropical cyclone genesis. It is within this disturbance that the critical layer, which acts to shield an incipient rotating disturbance from wind shear and dry air intrusion, is hypothesized to exist. To investigate this and other hypotheses, an ensemble-based framework based on the THORPEX Interactive Grand Global Ensemble (TIGGE), the Weather Research and Forecasting (WRF) model and an ensemble Kalman filter will be prepared in Year 1 for real-time prediction during the PREDICT field experiment (mid-Year 2). During the field experiment observations will be performed of tropical cyclones developing and travelling through the eastern Atlantic and Caribbean regions. Ensemble-based techniques will be used to diagnose the important physical processes, and the associated scales and parameters, that need to be sampled to optimally limit prediction errors. The remainder of Year 2 and all of Year 3 will be devoted to analysis of observations using models as diagnostic tools, including production of a convective-scale reanalysis that will include all data collected during the field campaign and will be used by other PREDICT PIs for their research. The intellectual merit of the proposed research lies in the potential to advance understanding of the processes and predictability associated with tropical cyclone genesis, via multi-scale, multi-faceted numerical model ensembles. The broader impacts of the proposed activity include improved probabilistic predictions of tropical cyclone genesis, and subsequent intensity change. Better forecasts of wind and rainfall structure at landfall will be possible. Two graduate students will be trained to run models that capture inner-core processes within tropical cyclones and to address unsolved issues related to their predictability. Furthermore, data and results will be shared between the research and operational communities.

该奖项是根据2009年美国复苏和再投资法案（公法111-5）资助的。这项研究是热带云系统大萧条前调查（PREDICT）项目的一部分。本研究的核心科学假设是，数值预报模式正确模拟天气尺度、热带前低气压槽的初始形成是准确模拟热带气旋形成的关键。假设在这个扰动中存在临界层，它的作用是屏蔽早期旋转扰动免受风切变和干空气入侵的影响。为了研究这一假设和其他假设，将在第一年准备一个基于THORPEX交互式大全球集合（TIGGE）、天气研究与预报（WRF）模型和一个集合卡尔曼滤波器的基于集合的框架，以便在PREDICT现场实验（第二年中期）期间进行实时预测。在实地试验期间，将对热带气旋的发展和穿越东大西洋和加勒比地区进行观测。基于集成的技术将用于诊断重要的物理过程，以及相关的尺度和参数，这些需要采样以最佳地限制预测误差。二年级的剩余时间和三年级的全部时间将致力于使用模型作为诊断工具对观测结果进行分析，包括对流尺度的再分析，该再分析将包括在实地活动期间收集的所有数据，并将用于其他PREDICT pi的研究。所提出的研究的智力价值在于，通过多尺度、多面数值模式集合，有可能促进对与热带气旋形成有关的过程和可预测性的理解。拟议活动的更广泛影响包括改进对热带气旋形成和随后的强度变化的概率预测。更好地预报登陆时的风和降雨结构将成为可能。两名研究生将接受培训，以运行捕捉热带气旋内部核心过程的模型，并解决与其可预测性相关的未解决问题。此外，数据和结果将在研究和业务社区之间共享。